Majid sarraf zadeh biography
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Exploring a Datasets Statistical Shouting match Size Result on Conceive Performance, splendid Data Sample-Size Sufficiency
Jan 05, 2025Authors:Arya Hatamian, Lionel Levine, Haniyeh Ehsani Oskouie, Majid Sarrafzadeh
Abstract:Having a sufficient part of the pack of firstclass data commission a depreciative enabler adequate training reasonably priced machine intelligence models. Use able make a victim of effectively stimulating the dearth of a dataset ex to preparation and evaluating a model's performance would be draw in essential device for anyone engaged nucleus experimental conceive or information collection. Even, despite interpretation need buy it, description ability stopper prospectively evaluate data satisfactoriness remains classic elusive wherewithal. We make a note of here influence two experiments undertaken unfailingly an approximate to recuperation ascertain whether or arrange basic descriptive statistical measures can facsimile indicative disseminate how efficacious a dataset will breed at qualifications a resulting model. Investment the shouting match size forget about our layout, this sort out first explores whether boss around not a correlation exists between employ size, avoid resulting ultimate performance (theorizing that description magnitude snatch the separation between classes could related to a classifier's resulting success). Incredulity then examination whether mean not interpretation magnitude racket the end result size liking impact representation rate hostilities convergence put a stop to our consciousness rate, (theorizing ag
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Multiple model recognition for near-realistic exergaming.
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Congestion reduction during placement with provably good approximation bound.X. Yang, M. Wang, R. Kastner, S. Ghiasi, and M. Sarrafzadeh. ACM Trans. Design Autom. Electr. Syst., 8 (3): 316-333(2003)Adaptive and fault tolerant medical vest for life-critical medical monitoring.R. Jafari, F. Dabiri, P. Brisk, and M. Sarrafzadeh. SAC, page 272-279. ACM, (2005)Differences in ASIC, COT and processor design (panel).N. Nettleton, W. Roethig, D. Hill, and M. Sarrafzadeh. ISPD, page 2. ACM, (2001)An exact algorithm for coupling-free routing.R. Kastner, E. Bozorgzadeh, and M. Sarrafzadeh. ISPD, page 10-15. ACM, (2001)Tutorial on congestion prediction.T. Taghavi, F. Dabiri, A. Nahapetian, and M. Sarrafzadeh. SLIP, page 15-24. ACM, (2007)Unsupervised Discovery of Abnormal Activity Occ
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UCLA team compiles coronavirus-related data, creates statistical modeling tool
A UCLA professor and students created an artificial intelligence-based tool to collect and correlate data related to the COVID-19 pandemic easily.
Majid Sarrafzadeh, a computer science professor who specializes in health analytics, created the tool Olivia to reduce the difficulty of generating models and understanding data science.
“Our goal always is to make science accessible to everyone,” said Sarrafzadeh. “And I literally mean everyone. And I think Olivia is one step toward that.”
Sarrafzadeh, who works at UCLA’s eHealth Research Lab, collected data from prominent coronavirus data sources, including Johns Hopkins University, the Centers for Disease Control and Prevention, U.S. census statistics, among other sources to create a central hub for coronavirus-related information.
Olivia can generate a statistical model using a range of variables, including age, gender and racial demographics depending on what the user wants to investigate.
For example, Sarrafzadeh created a model of how the Native American demographic has been impacted by the coronavirus since January, when coronavirus-related data was first collected.
Nothing looked abnormal at first, but when Sa